CCNet:面向多光谱图像的高速船只检测级联卷积神经网络（英文）

CCNet: A high-speed cascaded convolutional neural network for ship detection with multispectral images

针对实现遥感图像中船只目标的快速检测提出了一个采用多光谱图像、基于级联的卷积神经网络(CNN)船只检测方法CCNet.该方法所采用两级级联的CNN依次实现感兴趣区域(ROI)的快速搜索、基于感兴趣区域的船只目标定位和分割.同时采用含有更多细节信息的多光谱图像作为 CCNet 的输入能够提升网络提取特征鲁棒性从而使得检测更加精确.基于 SPOT 6 卫星多光谱图像的实验表明与当前主流的深度学习船只检测方法相比该方法能够在实现高检测精准度的基础上将检测速度提高 5 倍以上.

A novel ship detection method using cascaded convolutional neural network ( CCNet) with multispectral image is proposed to achieve high-speed detection. The CCNet employs two cascaded convolutional neural networks ( CNN) for extracting regions of interest ( ROIs),locating and segmenting ship objects sequentially. Benefit from the abundant details of the multispectral image,CCNet can extract more robust feature for achieving more accurate detection. The efficiency of CCNet has been validated by the experiments on the SPOT 6 satellite multispectral images. Compared with the state-of-the-art deep-learning-based ship detection algorithms,the proposed ship detection algorithm accelerates the processing by more than 5 times with a higher detection accuracy.